ByteDance Seedream 5.5 coming soon with big improvement. 100 Layer Scene Decomposition (Up from 10+ in 5.0 Pro): Splits dense posters, presentation slides, and motion templates into up to 100 individual raster layers with automatic occlusion inpainting behind extracted foreground objects. 80+ Rounds of Drift Resistant Local Editing: Locks unedited pixels (product geometry, facial identity, brand labels) across 80+ sequential single-variable editing passes (background swap -> relighting -> material change -> text addition) to minimize VAE re encoding degradation. Native 4K Output at 16:9 (Up from 2K Cap in 5.0 Pro): Generates native ⁠3840x2160⁠ widescreen frames, eliminating the 2K (⁠~2.36MP⁠) resolution bottleneck on 5.0 Pro for widescreen banners and multi panel storyboard grids. Native 4 Channel RGBA Generation (With Partial Alpha): Outputs true transparent PNG assets directly from the initial prompt, supporting semi transparent alpha values for glass refraction, soft contact shadows, sheer fabric, and fine hair strands without post generation background removal. Optical Font & Style Recognition for Text Re-Rendering: Reads existing font families, weights, colors, and layout hierarchy inside an image so you can translate or swap campaign copy (across English, Chinese, Japanese, and Thai) and re render matching typography in place. Sub Headline & Micro Copy Multilingual Fidelity: Sharpens stroke accuracy on dense, small-scale text specifically pricing, units, dates, bottle volume markings, and promotional fine print where 5.0 Pro was limited to larger poster headlines. Relighting Consistent Material & Portrait Shaders: Upgrades surface physics for ceramic micro texture, brushed metal anisotropy, glass refraction, and skin pores so that shifting lighting conditions (such as daylight to nighttime) updates reflections and contact shadows without smoothing skin texture or warping product labels. Role Assigned Multi Reference & Negative-Space Planning: Formalizes reference routing by assigning distinct jobs to each input image (Reference A = subject identity, Reference B = color palette, Reference C = pose, Reference D = layout) while automatically reserving clean negative space for headlines and CTAs.